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9th International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Mahendran, S. Radha Dogra, Akriti Mendhe, Dinesh Babu, S. B. G. Tilak Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology And Advanced Studies Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India Rutgers State University of New Jersey Office of Research Computing New BrunswickNJ United States Aditya Engineering College Department of Ece Surampalem India School of Computer Science Engineering Vellore Institute of Technology Tamil Nadu Vellore India Upums Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350365092

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. This paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. The suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. The study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level. © 2024 ieee.

关键词： Proteins

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Guest Editorial for the 17th Asia Pacific bioinformatics conference

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2123-2124页

作者： Zhang, Louxin Peng, Shaoliang Chen, Yi-Ping Phoebe Sankoff, David Li, Guoliang Zhang, Hong-Yu Natl Univ Singapore Dept Math Singapore 119076 Singapore Hunan Univ Natl Supercomp Ctr Wuhan 410082 Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Bundoora Vic 3086 Australia Univ Ottawa Dept Math & Stat Ottawa ON K1N 6N5 Canada Huazhong Agr Univ Coll Informat Wuhan 430070 Peoples R China

The eight papers in this special section were presented at the 17th Asia Pacific bioinformatics conference (APBC), which was held in Wuhan, China, 14-16 January 2019.

关键词： Special issues and sections Meetings bioinformatics

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Turing Computation with Neural Networks Composed of Synfire Rings

Turing Computation with Neural Networks Composed of Synfire ...

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ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on computational intelligence (ieee WCCI) / International Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Cabessa, Jeremie Univ Paris 2 Pantheon Assas Lab Math Econ & Appl Microecon LEMMA F-75006 Paris France Czech Acad Sci Inst Comp Sci Prague 18207 8 Czech Republic

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Synfire rings are fundamental neural circuits capable of conveying self-sustained activities in a robust and temporally precise manner. We propose a Turing-complete paradigm for neural computation based on synfire rings. More specifically, we provide an algorithmic procedure which, for any fixed-space Turing machine, builds a corresponding Boolean neural network composed of synfire rings capable of simulating it. As a consequence, any fixed-space Turing machine with tapes of length N can be simulated in linear time by some Boolean neural network composed of O(N) rings and cells. The construction can naturally be extended to general Turing machines. Therefore, any Turing machine can be simulated in linear time by some Boolean neural network composed of infinitely many synfire rings. The linear time simulation relies on the possibility to mimic the behavior of the machines. In the long term, these results might contribute to the realization of biological neural computers.

关键词： Computers Turing machines computational modeling Biological system modeling Neural circuits Cells (biology) Logic gates

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Parallel Computation of Conformational Stability for CD4+ T-cell Epitope Prediction

Parallel Computation of Conformational Stability for CD4+ T-...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Bhattacharya, Avik Wrabl, James O. Landry, Samuel J. Mettu, Ramgopal R. Tulane University Department of Computer Science New Orleans United States Johns Hopkins University Department of Biology Baltimore United States Tulane University School of Medicine Department of Biochemistry and Molecular Biology New Orleans United States

ISBN: (纸本)9798350337488

Antigen-presenting cells present peptides derived from foreign pathogens in the form of peptide-MHCII complexes to induce immune response. Two important and interrelated processes that lead to peptide presentation are antigen processing and peptide-MHCII binding. Although the latter phenomenon has been thoroughly studied and incorporated into CD4+ T cell epitope prediction tools, antigen processing has remained under-explored as a means to improve epitope prediction tools. In prior work, our group developed the antigen processing likelihood (APL) metric [1] and showed that it can be used to effectively predict CD4+ T-cell epitopes either alone or in combination with peptide-MHCII binding affinity-based tools. A key component of the APL scoring approach is a residue-wise conformational stability metric known as COREX [2], which estimates the likelihood of unfolding at each residue using a free energy approximation. COREX calculation takes a significant amount of time (6-8 hours) and thus limits our ability to rapidly predict CD4+ T-cell epitopes over large antigen sets. In this paper, we give a parallel algorithm to compute the COREX metric and demonstrate its utility over several benchmarks. We achieve a 196-fold speedup with our parallel algorithm, bringing the overall computation of APL down from hours to minutes. © 2023 ieee.

关键词： antigen processing CD4+ T cell response computational epitope prediction free energy calculation parallel computation protein structure

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A Topological Characterization of DNA Sequences Based on Chaos Geometry and Persistent Homology

A Topological Characterization of DNA Sequences Based on Cha...

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2022 International conference on computational Science and computational intelligence, CSCI 2022

作者： Quan Ngoc Nguyen, Dong Dong Tan Le, Phuong Xing, Lin Lin, Lizhen Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing101408 China University of Waterloo Department of Applied Mathematics Waterloo Canada University of Notre Dame Department of Applied and Computational Mathematics and Statistics Notre Dame United States

ISBN: (纸本)9798350320282

Methods for analyzing similarities among DNA sequences play a fundamental role in computational biology, and have a variety of applications in public health, and in the field of genome analysis. In this paper, a novel geometric and topological method for analyzing similarities among DNA sequences is developed, based on persistent homology from algebraic topology, in combination with chaos geometry in 4-dimensional space as a graphical representation of DNA sequences. Our topological framework for DNA similarity analysis is general, alignment-free, and can deal with DNA sequences of various lengths, while proving first-of-the-kind visualization features for visual inspection of DNA sequences directly, based on topological features of point clouds that represent DNA sequences. As an application, we test our methods on three datasets including genome sequences of different types of Hantavirus, Influenza A viruses, and Human Papillomavirus. © 2022 ieee.

关键词： Geometry

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Data preparation for artificial intelligence in medical imaging: Experiences from the ProCAncer-I initiative

Data preparation for artificial intelligence in medical imag...

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2023 ieee EMBS Special Topic conference on Data Science and Engineering in Healthcare, Medicine and biology, ieeeCONF 2023

作者： Kalokyri, Varvara Tachos, Nikolaos Sfakianakis, Stelios Nikiforaki, Katerina Karatzanis, Ioannis Kondylakis, Haridimos Mazzetti, Simone Regge, Daniele Papanikolaou, Nikolaos Marias, Konstantinos Fotiadis, Dimitrios Tsiknakis, Manolis FORTH-ICS Foundation of Research and Technology Hellas Institute of Computer Science Nik. Plastira 100 Crete Heraklion70013 Greece University Campus of Ioannina FORTH-BRI Ioannina45110 Greece University of Turin Department of Surgical Sciences Turin Italy Candiolo10060 Italy Champalimaud Foundation Champalimaud Research Computational Clinical Imaging Group Lisbon1400-038 Portugal FORTH-BRI Greece The Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department University Campus of Ioannina Ioannina45110 Greece

ISBN: (纸本)9798350383386

The ProCAncer-I project, an important initiative in the field of prostate cancer research, is exploiting the power of Artificial intelligence (AI) to advance prostate cancer diagnosis, prognosis, and treatment. The success of AI in prostate cancer imaging within this context relies on meticulous data preparation, especially when dealing with clinical data that need to be integrated with imaging data. This paper explores the importance of data preparation to implement AI tools for prostate cancer imaging, and outlines the approach followed and challenges needed to be addressed in the context of ProCAncer-I, for ensuring data quality, privacy, and reliability. © 2023 ieee.

关键词： Medical imaging

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An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第1期19卷 87-94页

作者： Nguyen, Trinh-Trung-Duong The-Anh Tran Le, Nguyen-Quoc-Khanh Dinh-Minh Pham Ou, Yu-Yen Yuan Ze Univ Dept Comp Sci & Engn Chungli 32003 Taiwan Viettel Cyber Secur Hanoi 550000 Vietnam Taipei Med Univ Coll Med Artificial Intelligence Med Taipei 106 Taiwan Taipei Med Univ Res Ctr Artificial Intelligence Med Taipei 106 Taiwan Vietnam Acad Sci & Technol Inst Biotechnol Hanoi 100000 Vietnam

It is well-known that the major reason for the rapid proliferation of cancer cells are the hypomethylation of the whole cancer genome and the hypermethylation of the promoter of particular tumor suppressor genes. Locating 5-methylcytosine (5mC) sites in promoters is therefore a crucial step in further understanding of the relationship between promoter methylation and the regulation of mRNA gene expression. High throughput identification of DNA 5mC in wet lab is still time-consuming and labor-extensive. Thus, finding the 5mC site of genome-wide DNA promoters is still an important task. We compared the effectiveness of the most popular and strong machine learning techniques namely XGBoost, Random Forest, Deep Forest, and Deep Feedforward Neural Network in predicting the 5mC sites of genome-wide DNA promoters. A feature extraction method based on k-mers embeddings learned from a language model were also applied. Overall, the performance of all the surveyed models surpassed deep learning models of the latest studies on the same dataset employing other encoding scheme. Furthermore, the best model achieved AUC scores of 0.962 on both cross-validation and independent test data. We concluded that our approach was efficient for identifying 5mC sites of promoters with high performance.

关键词： Natural language processing k-mers embedding DNA 5-methylcytosine site prediction promoter advanced machine learning classifiers

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Use Chou's 5-Steps Rule With Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第2期19卷 1235-1244页

作者： Nguyen, Trinh-Trung-Duong Ho, Quang-Thai Le, Nguyen-Quoc Khanh Dinh-Phan, Van Ou, Yu-Yen Yuan Ze Univ Dept Comp Sci & Engn Chungli 32003 Taiwan Taipei Med Univ Coll Med Profess Master Program Artificial Intelligence Me Taipei 106 Taiwan Taipei Med Univ Res Ctr Artificial Intelligence Med Taipei 106 Taiwan Univ Danang Univ Econ Da Nang 550000 Vietnam

Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences.

关键词： Proteins Natural language processing Feature extraction Predictive models Two dimensional displays Tools Prediction algorithms Electron transport protein machine learning natural language processing support vector machine word embedding

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A Prototype System Design for Identification and Classification of Stimulation Sites Inducing Hand Motor Function

A Prototype System Design for Identification and Classificat...

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ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Gong Chen Ning Tian Xueliang Bao Shiqin Liu key laboratory of artificial intelligence and information security for channeling computing resources from the east to the west School of Information Engineering Ningxia university Yinchuan Ningxia China

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

To address the challenges of surface electrical stimulation using large electrodes, which are not capable of selectively activating muscles and may induce varying muscle responses with variations in body posture, this study proposed a system including hardware platform and a PC software, which can identify and classify stimulation sites. The system's hardware platform is a surface functional electrical stimulation (FES) device with array electrodes, while the PC software is used for stimulation parameter configuration and limb movement trajectory data acquisition based on Leap Motion and ESP8266. To test the system's performance, wrist extension and flexion movements were performed. The results demonstrate that the system can establish parameters for stimulation sites in the array electrodes and capture the motion trajectories of wrist extension and wrist flexion in real-time during limb movement. This system establishes a platform for creating a database of limb movement trajectories and identifying and categorizing stimulation sites for target movements in various body postures using deep learning algorithms.

关键词： Electrodes Wrist Training Limbs Muscles Iron Hardware Trajectory Arrays Neuromuscular stimulation

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Quantifying statistics of gene product copy-number fluctuations: A stochastic hybrid systems approach 63

Quantifying statistics of gene product copy-number fluctuati...

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63rd ieee conference on Decision and Control, CDC 2024

作者： Vahdat, Zahra Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States University of Delaware Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology NewarkDE19716 United States

ISBN: (纸本)9798350316339

Across diverse cell types, genes are often expressed at low levels resulting in significant stochastic fluctuations (noise) in intracellular copy number of mRNAs and proteins over time. Motivated by single-cell experiments, we capture this stochasticity by considering a given gene that toggles between transcriptionally active and inactive states with the time spent in each state being an arbitrary random variable. mRNAs are synthesized from the active state as per a Poisson process and each molecule degrades after a random lifespan (i.e., random time from birth to death). Modeling this process using the Stochastic Hybrid System (SHS) formalism we derive exact analytical results for the statistical moments of molecular counts. Our results show that for fixed mean mRNA numbers, fluctuations in mRNA levels are amplified with decreasing noise in mRNA lifespan. Relaxing the Poisson process assumption, we next consider a scenario where transcription events occur such that the time between successive events is an arbitrarily distributed random variable. In this case, we show that decreasing noise in mRNA lifespan can both increase/decrease mRNA count fluctuations depending on the underlying transcriptional process. Finally, we extend these results to the protein level, where increasing noise in mRNA counts is sometimes associated with decreasing noise in protein copy numbers. © 2024 ieee.

关键词： Stochastic systems

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